Optimizing Database Design with Genetic Algorithms

This study utilizes genetic algorithms to optimize database design. Given the increasing complexity of distributed systems and the need for faster query execution, these algorithms are presented as an effective tool for improving database performance. The research begins with extracting features related to database design, such as the number of tables, joins, and system resource utilization, followed by generating and evaluating different database structures using genetic algorithms. Evaluations are based on query response times, system resource consumption, and server load distribution. The results show that genetic algorithms significantly reduce query response times, lower CPU and memory usage, and enhance load balancing efficiency. Specifically, for complex queries, the optimizations lead to substantial reductions in CPU and memory consumption, as well as a notable improvement in load distribution. This research demonstrates that genetic algorithms can effectively optimize database design and contribute to overall system performance enhancement.

Keywords:

Genetic algorithms , database optimization , query response time , system resource utilization , load balancing , database design